Detecting Informal Organization Through Data Mining Techniques
This addresses the challenge for managers in human resources to detect and manage informal organizations, which is often complicated or impossible, but the approach is incremental as it applies existing data mining methods to a new domain.
The study tackled the problem of recognizing informal organizations in human resources management by applying data mining techniques like factor analysis, clustering, classification, and association rule mining to employee data, resulting in a model for identifying similar characteristics among people to optimize recognition.
One of the main topics in human resources management is the subject of informal organizations in the organization such that recognizing and managing such informal organizations play an important role in the organizations. Some managers are trying to recognize the relations between informal organizations and being a member of them by which they could assist the formal organization development. Methods of recognizing informal organizations are complicated and occasionally even impossible. This study aims to provide a method for recognizing such organizations using data mining techniques. This study classifies indices of human resources influencing the creation of informal organizations, including individual, social, and work characteristics of an organizations employees. Then, a questionnaire was designed and distributed among employees. A database was created from obtained data. Applied data mining techniques in this study are factor analysis, clustering by K-means, classification by decision trees, and finally association rule mining by GRI algorithm. At the end, a model is presented that is applicable for recognizing the similar characteristics between people for optimal recognition of informal organizations and usage of this information.